def get_candidate_documents(model,
batch_size,
dev_set=None,
k_best=5,
max_doc_len=-1,
num_padding_tokens=0):
"""Get list of candidate documents for each pattern, from which best match will be selected"""
num_patterns = model.total_num_patterns
selected_documents = [[] for i in range(num_patterns)]
for batch in chunked(dev_set, batch_size):
batch_obj = Batch([x[0][0] for x in batch], model.embeddings,
model.to_cuda, 0, max_doc_len)
_, scores = model.forward(batch_obj, 1, 0)
scores = scores.data
# Adding epsilon to scores in order to not have two documents with the same score
epsilon = (torch.rand(scores.size()) - 0.5) / 10000
scores += model.to_cuda(epsilon)
for i in range(num_patterns):
for j in range(batch_obj.size()):
tup = (scores[j,
i], (batch[j][0][1], batch[j][0], batch[j][1]))
# print(i,j,"tup=",scores[j,i], epsilon[j,i], scores2[j,i])
heapq.heappush(selected_documents[i], tup)
if len(selected_documents[i]) == (k_best + 1):
heapq.heappop(selected_documents[i])
return selected_documents
def test_can_use_iterator_node(self):
iterable = chunked(StringIO(data_source['mary']), chunksize=3)
class D(BaseModel, self.Settings):
stream = Feature(IteratorNode, store=True)
words = Feature(Tokenizer, needs=stream, store=False)
count = JSONFeature(WordCount, needs=words, store=True)
_id = D.process(stream=iterable)
doc = D(_id)
self.assertEqual(data_source['mary'], doc.stream.read())
def _process(self, data):
try:
flo = StringIO(data_source[data])
except KeyError as e:
if isinstance(data, str):
flo = StringIO(data)
else:
raise e
for chunk in chunked(flo, chunksize=self._chunksize):
yield chunk
def __iter__(self, flo):
metadata, _ = NumpyMetaData.unpack(flo)
example_size = metadata.totalsize
chunk_size = int(example_size * self.n_examples)
count = 0
for chunk in chunked(flo, chunk_size):
n_examples = len(chunk) // example_size
yield _np_from_buffer(chunk, (n_examples, ) + metadata.shape,
metadata.dtype)
count += 1
if count == 0:
yield _np_from_buffer(buffer(''), (0, ) + metadata.shape,
metadata.dtype)
def create_placement_groups(job_id, node_list, partition_name):
PLACEMENT_MAX_CNT = 150
groups = {
f"{cfg.slurm_cluster_name}-{partition_name}-{job_id}-{i}": nodes
for i, nodes in enumerate(chunked(node_list, n=PLACEMENT_MAX_CNT))
}
reverse_groups = {node: group for group, nodes in groups.items() for node in nodes}
model = next(iter(node_list))
region = lkp.node_region(model)
requests = {
group: create_placement_request(group, region)
for group, incl_nodes in groups.items()
}
done, failed = batch_execute(requests)
if failed:
reqs = [f"{e}" for _, e in failed.values()]
log.fatal("failed to create placement policies: {}".format("\n".join(reqs)))
log.info(f"created {len(done)} placement groups ({','.join(done.keys())})")
return reverse_groups
def interpret_documents(model, batch_size, dev_data, dev_text, ofile,
max_doc_len):
j = 0
with open(ofile, "w") as ofh:
for batch_idx, chunk in enumerate(chunked(dev_data, batch_size)):
batch = Batch([x for x, y in chunk], model.embeddings,
model.to_cuda)
res, scores = model.forward(batch, 1)
print("ss", scores.size())
output = softmax(res).data
predictions = [int(x) for x in argmax(output)]
num_patts = scores.size()[1]
diffs = np.zeros((num_patts, batch.size()))
# Traversing all patterns.
for i in range(num_patts):
# Copying scores data to numpy array.
scores_data = np.array(scores.data.numpy(), copy=True)
# Zeroing out pattern number i across batch
scores_data[:, i] = 0
# Running mlp.forward() with zeroed out scores.
forwarded = softmax(
model.mlp.forward(Variable(
torch.FloatTensor(scores_data)))).data.numpy()
# Computing difference between forwarded scores and original scores.
for k in range(batch.size()):
# diffs[i,k] = output[k, predictions[k]] - \
# output[k, 1 - predictions[k]] - \
# forwarded[k, predictions[k]] + \
# forwarded[k, 1 - predictions[k]]
diffs[i, k] = forwarded[k, 1 - predictions[k]] - output[
k, 1 - predictions[k]]
# Now, traversing documents in batch
for i in range(batch.size()):
# Document string
text_str = str(" ".join(dev_text[j]).encode('utf-8'))[2:-1]
# Top ten patterns with largest differences between leave-one-out score and original score.
top_ten_deltas = sorted(enumerate(diffs[:, i]),
key=lambda x: x[1],
reverse=True)[:10]
top_ten_neg_deltas = sorted(enumerate(diffs[:, i]),
key=lambda x: x[1])[:10]
# Top ten patterns with largest overall score (regardless of classification)
top_ten_scores = sorted(enumerate(scores.data.numpy()[i, :]),
key=lambda x: x[1],
reverse=True)[:10]
top_scoring_spans = get_top_scoring_spans_for_doc(
model, dev_data[j], max_doc_len)
# Printing out everything.
ofh.write(
"{} {} {} All in, predicted: {:>2,.3f} All in, not-predicted: {:>2,.3f} Leave one out: +res: {} -res: {} Patt scores: {}\n"
.format(
dev_data[j][1], predictions[i], text_str,
output[i, predictions[i]],
output[i, 1 - predictions[i]], " ".join([
"{}:{:>2,.3f}".format(i, x)
for (i, x) in top_ten_deltas
]), " ".join([
"{}:{:>2,.3f}".format(i, x)
for (i, x) in top_ten_neg_deltas
]), " ".join([
"{}:{:>2,.3f}".format(i, x)
for (i, x) in top_ten_scores
])))
ofh.write("Top ten deltas:\n")
for l in top_ten_deltas:
s = top_scoring_spans[l[0]].display(dev_text[j])
ofh.write(
str(int(l[0])) + " " + str(s.encode('utf-8'))[2:-1] +
"\n")
ofh.write("Top ten negative deltas:\n")
for l in top_ten_neg_deltas:
s = top_scoring_spans[l[0]].display(dev_text[j])
ofh.write(
str(int(l[0])) + " " + str(s.encode('utf-8'))[2:-1] +
"\n")
j += 1
def _generator(self, stream, content_length):
if not content_length:
raise ValueError('content_length should be greater than zero')
for chunk in chunked(stream, chunksize=self._chunksize):
yield StringWithTotalLength(chunk, content_length)
def __iter__(self, flo):
self._total_length = struct.unpack('I', flo.read(4))[0]
for chunk in chunked(flo, self._chunksize):
yield StringWithTotalLength(chunk, self._total_length)
def __iter__(self, flo):
decompressor = bz2.BZ2Decompressor()
for chunk in chunked(flo):
yield decompressor.decompress(chunk)
def __iter__(self, flo):
for chunk in chunked(flo):
yield chunk
def _process(self, data):
flo = StringIO(data_source['mary'])
for chunk in chunked(flo, chunksize=3):
yield chunk
def _process(self, data):
flo = StringIO(data)
for chunk in chunked(flo, chunksize=self._chunksize):
yield chunk
def resume_nodes(nodelist, placement_groups=None, exclusive=False):
"""resume nodes in nodelist"""
def ident_key(n):
# ident here will refer to the combination of partition and group
return "-".join(
(
lkp.node_partition_name(n),
lkp.node_group_name(n),
)
)
# support already expanded list
nodes = nodelist
if isinstance(nodes, str):
nodelist = expand_nodelist(nodelist)
nodes = sorted(nodelist, key=ident_key)
if len(nodes) == 0:
return
grouped_nodes = {
ident: chunk
for ident, nodes in groupby(nodes, ident_key)
for chunk in chunked(nodes, n=BULK_INSERT_LIMIT)
}
log.debug(f"grouped_nodes: {grouped_nodes}")
# make all bulkInsert requests and execute with batch
inserts = {
ident: create_instances_request(nodes, placement_groups, exclusive)
for ident, nodes in grouped_nodes.items()
}
started, failed = batch_execute(inserts)
if failed:
failed_reqs = [f"{e}" for _, (_, e) in failed.items()]
log.error("bulkInsert API failures: {}".format("\n".join(failed_reqs)))
for ident, (_, exc) in failed.items():
down_nodes(grouped_nodes[ident], exc._get_reason())
# wait for all bulkInserts to complete and log any errors
bulk_operations = [wait_for_operation(op) for op in started.values()]
for bulk_op in bulk_operations:
if "error" in bulk_op:
error = bulk_op["error"]["errors"][0]
log.error(
f"bulkInsert operation error: {error['code']} operationName:'{bulk_op['name']}'"
)
# Fetch all insert operations from all bulkInserts. Group by error code and log
successful_inserts, failed_inserts = separate(
lambda op: "error" in op, get_insert_operations(bulk_operations)
)
# Apparently multiple errors are possible... so join with +.
# grouped_inserts could be made into a dict, but it's not really necessary. Save some memory.
grouped_inserts = util.groupby_unsorted(
failed_inserts,
lambda op: "+".join(err["code"] for err in op["error"]["errors"]),
)
for code, failed_ops in grouped_inserts:
# at least one insert failure
failed_nodes = [parse_self_link(op["targetLink"]).instance for op in failed_ops]
hostlist = util.to_hostlist(failed_nodes)
count = len(failed_nodes)
log.error(
f"{count} instances failed to start due to insert operation error: {code} ({hostlist})"
)
down_nodes(hostlist, code)
if log.isEnabledFor(logging.DEBUG):
msg = "\n".join(
err["message"] for err in next(failed_ops)["error"]["errors"]
)
log.debug(f"{code} message from first node: {msg}")
# If reconfigure enabled, create subscriptions for successfully started instances
if lkp.cfg.enable_reconfigure and len(successful_inserts):
started_nodes = [
parse_self_link(op["targetLink"]).instance for op in successful_inserts
]
count = len(started_nodes)
hostlist = util.to_hostlist(started_nodes)
log.info("create {} subscriptions ({})".format(count, hostlist))
execute_with_futures(subscription_create, nodes)
